Abstract A058: Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity
Authors: Yun Jae Yoo, Ki H Oh, Luke A. Torre-Healy, Richard A. Moffitt
Journal: Cancer Research (2022)

Abstract Background: Genetically engineered mouse models (GEMMs) are widely used in the study of pancreatic ductal adenocarcinoma (PDAC) because of their immune-competent tumor microenvironment (TME); however, the extent to which particular GEMMs recapitulate the tumor and TME observed in the patient population has not been systematically evaluated. In this study, we integrate single-cell RNA sequencing (sc-RNA-seq) data from multiple studies and multiple GEMM backgrounds to identify differences in the cellular compositions of popular PDAC GEMMs. Methods: A total of 49,191 cells were used from three studies, including normal mouse pancreas (N=2) and five different GEMM backgrounds (N=16). Data curation, integration, and analysis were based on the Seurat pipeline in R. SingelCellNet was used to train a random forest model on manually labeled human sc-RNA-seq data from 20 patients. To enable cross-species use, the classifier was trained using only genes with both human and mouse homologues. Cells classified as neoplastic were further clustered to quantify the number of classical and basal-like cells based on signature gene expression levels. The ratio of these subtypes in each GEMM and the relationship between the modified genes in each model were examined. Results: Ad-hoc clustering and a human-cell-trained single-cell classifier showed 79% agreement in an integrated data set of PDAC GEMMs. Cells identified by both methods as tumor (8,303 cells, 17% of total) were assessed for PDAC tumor subtype via subsequent clustering analysis (basal-like or classical). When comparing the ratio of differently subtyped tumor cells, we identified stark differences between GEMM genetic backgrounds. Among five different models, KIC (KrasLSL−G12D/+Ink4a/Arffl/flPtf1aCre/+), KPPCN (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+Nsdhlfl/fl), and pdx1-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Pdx1Cre/+) exhibited a higher proportion of basal-like PDAC cells compared to KPfC/KPPC (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+) and ptf1a-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Ptf1aCre/+). Interestingly, in the KIC model, which was harvested at early and late time points (40 or 60 days), classical PDAC was overrepresented in early models, and basal-like PDAC was more prevalent in the older tumors. While sample sizes are limited in this study, in Pdx1 driven models, we observed a bias towards basal-like phenotype in GEMMs using the Trp53LSL-R172H/+ method compared to those with Trp53fl/fl. Conclusions: In a comparison of publicly available sc-RNA-seq, we highlight potential biases in the molecular subtypes that arise from specific PDAC GEMMs. Because of the known link between tumor subtype and therapeutic response, these results suggest translational work may benefit from GEMM selection that considers transcriptomic diversity. Citation Format: Yun Jae Yoo, Ki H Oh, Luke A. Torre-Healy, Richard A. Moffitt. Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A058.